H2O自动编码器含N个隐藏层的结构及奇偶层分配问询

Let’s walk through how H2O structures its autoencoders, especially when you have an even or odd number of hidden layers. I’ll use your exact examples to make this concrete.

General Structure of H2O Autoencoders with N Hidden Layers #

H2O’s autoencoder follows a symmetric (or near-symmetric) pattern when you define your hidden layers. The key points:

• The list of hidden layers you specify gets split into encoder layers, an optional bottleneck layer (for odd hidden layer counts), and decoder layers.
• Input and output layers are automatically added by H2O: the input layer matches your dataset’s feature count, and the output layer mirrors the input (since autoencoders reconstruct the original input).

Example 1: 4 Hidden Layers ([10, 10, 10, 10]) #

Since we have an even number of hidden layers (4), the split is clean:

• Encoder: Uses the first 2 hidden layers. So the encoder’s full structure is input layer → 10-neuron layer → 10-neuron layer — that’s 2 hidden layers total for the encoder (input layer is auto-added, not part of your specified list).
• Decoder: Uses the last 2 hidden layers. Its full structure is 10-neuron layer → 10-neuron layer → output layer — another 2 hidden layers for the decoder (output layer is also auto-added).

Your initial understanding is spot-on here: encoder includes the input layer plus 2 hidden layers, decoder includes 2 hidden layers plus the output layer.

Example 2: 3 Hidden Layers ([10, 10, 10]) #

With an odd number of hidden layers (3), we have a bottleneck layer in the middle:

• Encoder: Uses the first 1 hidden layer. Full structure: input layer → 10-neuron layer — 1 hidden layer for the encoder.
• Bottleneck Layer: The middle 10-neuron layer. This is the compressed representation of the input, acting as the bridge between encoder and decoder.
• Decoder: Uses the last 1 hidden layer. Full structure: 10-neuron layer → output layer — 1 hidden layer for the decoder.

So the full model flow is: Input → Encoder Hidden Layer 1 → Bottleneck → Decoder Hidden Layer 1 → Output

Quick Recap for Any N #

• If N (number of specified hidden layers) is even:
  • Encoder gets first N/2 hidden layers
  • Decoder gets last N/2 hidden layers
• If N is odd:
  • Encoder gets first (N-1)/2 hidden layers
  • Middle layer is the bottleneck
  • Decoder gets last (N-1)/2 hidden layers

内容的提问来源于stack exchange，提问作者stacy1991